"regret analysis" Papers
20 papers found
Conference
ADAM Optimization with Adaptive Batch Selection
Gyu Yeol Kim, Min-hwan Oh
MALinZero: Efficient Low-Dimensional Search for Mastering Complex Multi-Agent Planning
Sizhe Tang, Jiayu Chen, Tian Lan
Online Two-Stage Submodular Maximization
Iasonas Nikolaou, Miltiadis Stouras, Stratis Ioannidis et al.
Pareto Optimal Risk-Agnostic Distributional Bandits with Heavy-Tail Rewards
Kyungjae Lee, Dohyeong Kim, Taehyun Cho et al.
Precise Asymptotics and Refined Regret of Variance-Aware UCB
Yingying Fan, Yuxuan Han, Jinchi Lv et al.
Regret Analysis of Average-Reward Unichain MDPs via an Actor-Critic Approach
Swetha Ganesh, Vaneet Aggarwal
Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control
Bruce D. Lee, Leonardo F. Toso, Thomas T. Zhang et al.
Second Order Bounds for Contextual Bandits with Function Approximation
Aldo Pacchiano
Toward Understanding In-context vs. In-weight Learning
Bryan Chan, Xinyi Chen, Andras Gyorgy et al.
True Impact of Cascade Length in Contextual Cascading Bandits
Hyun-jun Choi, Joongkyu Lee, Min-hwan Oh
A General Online Algorithm for Optimizing Complex Performance Metrics
Wojciech Kotlowski, Marek Wydmuch, Erik Schultheis et al.
High-dimensional Linear Bandits with Knapsacks
Wanteng Ma, Dong Xia, Jiashuo Jiang
Matroid Semi-Bandits in Sublinear Time
Ruo-Chun Tzeng, Naoto Ohsaka, Kaito Ariu
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization
Kwang-Sung Jun, Jungtaek Kim
On Multi-Armed Bandit with Impatient Arms
Yuming Shao, Zhixuan Fang
Provable Interactive Learning with Hindsight Instruction Feedback
Dipendra Misra, Aldo Pacchiano, Robert Schapire
Provably Efficient Partially Observable Risk-sensitive Reinforcement Learning with Hindsight Observation
Tonghe Zhang, Yu Chen, Longbo Huang
Regret Analysis of Repeated Delegated Choice
Suho Shin, Keivan Rezaei, Mohammad Hajiaghayi et al.
Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach
Wen Huang, Xintao Wu
Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge
Meshal Alharbi, Mardavij Roozbehani, Munther Dahleh